STA/BST 290 Seminar: Yuedong Wang

STA 290 Seminar Series

Thursday, March 12th 2015, 4:10pm, MSB 1147 (Colloquium Room)

Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)

Speaker:          Yuedong Wang (UC Santa Barbara)

Title:                A Suite of S functions Implementing Spline smoothing Techniques

Abstract:          We present a suite of user friendly R functions for fitting various smoothing spline models including (a) non-parametric regression models for independent and correlated Gaussian data, and for independent binomial, Poisson and Gamma data;

(b) semi-parametric linear mixed-effects models;

(c) non-parametric nonlinear regression models;

(d) semi-parametric nonlinear regression models; and

(e) semi-parametric nonlinear mixed-effects models. The general form of smoothing splines based on reproducing kernel Hilbert spaces is used to model non-parametric functions. Thus these R functions deal with many different situations in a unified fashion. Well known special cases are polynomial, periodic, spherical, thin-plate and L splines, GAM, SS ANOVA, projection pursuit, multiple index, varying coefficient, functional linear and self-modeling nonlinear regression models.